Myoelectric prostheses, which rely on electromyography (EMG) signals to control joint movement, are often used to effectively treat upper-limb amputation. The control principles used in commercially available prostheses have been available for many decades and rely on an estimate of the amplitude of strategically placed electrodes coupled with simple rules to form control signals for controlling the operation of the prosthesis. Only a limited number of movements may be restored and to achieve a particular task for movement of the prosthesis, and the movements must be controlled sequentially as only one motion may be controlled at a time.
Pattern recognition has also been used to extract control signals for prosthetic limbs but these algorithms have yet to reach the marketplace. However, pattern recognition myoelectric control is still currently limited to controlling sequential movements, or pre-programmed coordinated movements about several joints in which each joint may not be controlled independently.
Other techniques, which rely on continuous projection of the signal energy onto subspaces, have been used to estimate simultaneous movements of the wrist and hand. The projection matrices have been determined using muscle synergy inspired ideas, or by using neural networks with little a priori information. These methods differ from pattern recognition in which signal patterns are described by multiple features and use methods which partition the feature space.